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Updated: Sep 9, 2025
14:15
Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing
Published on: November 18, 2014
12.0K
Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with
Hyun Joo Ji1,2, Mihaela Pertea3,4,5
1Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. hji20@jh.edu.
Genome Biology
|August 28, 2025
Summary
TranSigner enhances long-read RNA sequencing analysis by accurately assigning reads to transcripts and estimating their abundances. This new tool improves transcriptome profiling for researchers studying gene expression.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Long-read RNA sequencing offers full-length transcript information.
- Current transcriptome profiling methods using long-read data lack consistency in transcript identification and quantification.
Purpose of the Study:
- Introduce TranSigner, a novel computational tool for robust transcriptome profiling.
- Provide read-level support for transcript identification and abundance estimation from long-read RNA sequencing data.
Main Methods:
- TranSigner integrates three modules: read alignment to transcripts, compatibility scoring, and a guided expectation-maximization algorithm.
- The tool was validated using simulated and experimental data from Homo sapiens, Arabidopsis thaliana, and Mus musculus.
Main Results:
- TranSigner demonstrates accurate read assignment to specific transcripts.
- The tool provides reliable estimation of transcript abundances.
- Achieved consistent results across diverse organisms and data types.
Conclusions:
- TranSigner offers a significant advancement in analyzing long-read RNA sequencing data.
- The tool improves the accuracy and consistency of transcriptome profiling.
- Enables more reliable gene expression studies using full-length transcript information.
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